Browse
Submit Data
Databases
API
Help

Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Microarray analysis of DNA methylation in metastatic versus non metastatic breast cancers

ABSTRACT: 19 non-metastatic breast cancers (controls) and 19 breast cancers showing metastases or metastatic relapse within 5 years (cases) were extracted from a multi-institutional case series of 123 breast cancer patients . Cases and controls were analyzed for DNA methylation over 56 genes by the MethDet assay [1]using a dedicated microarray (MethDet56). 1. Levenson VV, Melnikov AA: The MethDet: a technology for biomarker development. Expert Rev Mol Diagn 2011, 11:807-812.

INSTRUMENT(S): GenePix 4000B Microarray Scanner (Molecular Devices).

ORGANISM(S): Homo sapiens

SUBMITTER: Emanuela Guerra

PROVIDER: E-MTAB-3153 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Publications

A seven-gene CpG-island methylation panel predicts breast cancer progression.

Li Yan Y Melnikov Anatoliy A AA Levenson Victor V Guerra Emanuela E Simeone Pasquale P Alberti Saverio S Deng Youping Y

BMC cancer 20150519

<h4>Background</h4>DNA methylation regulates gene expression, through the inhibition/activation of gene transcription of methylated/unmethylated genes. Hence, DNA methylation profiling can capture pivotal features of gene expression in cancer tissues from patients at the time of diagnosis. In this work, we analyzed a breast cancer case series, to identify DNA methylation determinants of metastatic versus non-metastatic tumors.<h4>Methods</h4>CpG-island methylation was evaluated on a 56-gene canc ...[more]

PMID: 25986046

Similar Datasets

Quantitative proteomic analysis identifies TUBB2A as a novel protein biomarker for prediction of distant metastatic breast cancer

Project description:Metastasis of breast cancer to other distant organs is fatal to patients. However, few studies have revealed biomarkers associated with distant metastatic breast cancer. Furthermore, the inability of current biomarkers such as HER2, ER and PR, in accurately differentiating between distant metastatic breast cancers from non-distant metastatic ones necessitates the development of novel biomarkers. An integrated proteomics approach that combines filter-aided sample preparation, tandem mass tag labeling (TMT), high pH fractionation, and high resolution MS was applied to acquire in-depth proteome data of distant metastatic breast cancer FFPE tissue. Bioinformatics analyses for gene ontology and signaling pathways using differentially expressed proteins (DEPs) were performed to investigate molecular characteristics of distant metastatic breast cancer. In addition, real-time polymerase chain reaction (RT-PCR) and invasion/migration assays were performed to validate the differential regulation and functional capability of biomarker candidates. A total of 9,459 and 8,760 proteins were identified from the pooled sample set and the individual sample set, respectively. Through our stringent criteria, TUBB2A was selected as a novel biomarker. The metastatic functions of the candidate were subsequently validated. Bioinformatics analysis using DEPs were able to characterize the overall molecular features of distant metastasis as well as investigate the differences across breast cancer subtypes. Our study is the first to explore the distant metastatic breast cancer proteome using FFPE tissue. The depth of our dataset enabled the discovery of novel biomarker and the investigation of proteomic characteristics of distant metastatic breast cancer. The distinct molecular features of breast cancer subtypes were also observed. Our proteomic data has important utility as a valuable resource for the research on distant metastatic breast cancer.

2020-06-05 | PXD016061 | Pride

miRNA profiling of late stage metastatic Her2+ breast cancer

Project description:We have assayed differential miRNA expression between late stage (stage IV) metastatic Her2+ and Her2- breast cancer. 4 late stage metastatic Her2- samples were compared to 3 late stage metastatic Her2+ samples. There are 4 within array replicates for each sample.

2010-04-29 | E-GEOD-20066 | biostudies-arrayexpress

Single cell RNA-seq of an native and engineered metastatic niche in a metastatic breast cancer murine model

Project description:Metastatic lesions are typically not found until patients self-report symptoms or they become radiologically evident. We have developed an engineered metastatic niche (scaffold) that recruits aggressive tumor cells prior to their colonization in other organs. The engineered niche can be monitored for dynamic gene expression, and changes at this site are analogous to those in a native metastatic site (lung) for triple negative breast cancer (4T1 cells). We were able to develop a 10-gene signature from the scaffold that accurately monitors disease progression and recurrence or resistance to resection therapy. This data set acts to dissect the heterogeneity of the cell populations in the engineered and native metastatic niche and identify the cell types that contribute to the success of the signature.

2022-08-02 | E-MTAB-8503 | biostudies-arrayexpress

MicroRNAome of highly metastatic and non-metastatic human breast cell lines

Project description:MicroRNAs are noncoding, endogenous small RNAs that regulate target genes by cleavage of the targeted mRNA or translational repression. We investigated the microRNAome using 2-color microarrays in a highly invasive human breast cancer cell line, MDA-MB-231 (sub line 4175) and a non-invasive breast epithelial cell line, MCF10A. We found 13 miRNAs that were up-regulated and 9 were down-regulated significantly in 4175 cells (p <0.05, fold change >2) compared with MCF10A cells. We compared the highly metastatic human breast cell lines MDA-MB-231 (4175 subline) with non-metastatic MCF10A cell lines. Two 4175 sublines and two MCF10A cell lines, independently grown and harvested. Dye swap was performed.

2010-05-15 | E-GEOD-17350 | biostudies-arrayexpress

Array CGH of breast cancer cells with distinct metastatic properties

Project description:Xenograft metastases from MDA-MB-231 cells and CN34 cells at different organs were collected and yielded sub-populations that exhibited increased metastagenicity to the lung or brain. Genomic aberrations of these subpopulations were profiled and compared to the parental populations. Array CGH of breast cancer cells with distinct metastatic properties

2010-12-12 | E-GEOD-23823 | biostudies-arrayexpress

Proteomics profiling in primary tumors of metastatic and non-metastatic breast cancers

Project description:Analysis of different proteomics profile in primary tumors of metastatic and non-metastatic breast cancers.

2023-03-09 | PXD037288 | Pride

Isolation and characterization of a metastatic hybrid cell line generated by ER negative and ER positive breast cancer cells in mouse bone marrow

Project description:The origin and the contribution of breast tumor heterogeneity to its progression are not clear. We investigated the effect of a growing orthotopic tumor formed by an aggressive estrogen receptor (ER)-negative breast cancer cell line on the metastatic potential of a less aggressive ER-positive breast cancer cell line for the elucidation of how the presence of heterogeneous cancer cells might affect each other’s metastatic behavior. ER positive ZR-75-1/GFP/puro cells, resistant to puromycin and non-tumorigenic/non-metastatic without exogenous estrogen supplementation, were injected intracardiacally into mice bearing growing orthotopic tumors, formed by ER negative MDA-MB-231/GFP/Neo cells resistant to G418. A variant cell line B6, containing both estrogen-dependent and -independent cells, were isolated from GFP expressing cells in the bone marrow and re-inoculated in nude mice to generate an estrogen-independent cell line B6TC.

2011-05-25 | E-GEOD-27515 | biostudies-arrayexpress

A nested case-control study to investigate drivers for metastatic disease in breast cancer

Project description:The purpose of this study was to improve prediction of patients at high-risk for metastatic disease utilizing a nested case-control design that uniquely enables enrichment for relevant phenotypes. We identified all women diagnosed with primary breast cancer from January 1, 1997, to December 31, 2005, in the Stockholm health care region. Patients developing distant metastatic disease (cases) were selected and controls (free from distant disease) were randomly matched by adjuvant therapy, age and calendar period at diagnosis. The nested case-control study included 768 study subjects with clinical information and gene expression arrays (Human Cancer G110). Study subjects were randomly and equally divided into training set (discovery) or testing (validation) set. Metastatic onset prediction was then compared including either clinical variables only or combining clinical and genetic information. Differentially expressed genes and pathways between cases and controls included a wide-spectrum of well known as well as candidate regulators of the metastatic cascade. The nested case-control study included 768 study subjects corresponding to 623 primary tumor samples. Details concerning case-control status are given in the samples section. Each case and its' matching controls form risk sets, indicated by the setnr variable.

2014-11-03 | E-GEOD-48091 | biostudies-arrayexpress

MDA-MB-231/1833 bone-metastatic breast cancer cell gene expression after conditioning in conditioned medium from HUVECs conditioned in conditioned medium from static and flow-stimulated MLO-Y4 osteocytes

Project description:Bone metastases is a common severe complication for breast cancer. We previously showed that conditioned medium (CM) from osteocytes stimulated with oscillatory fluid flow, mimicking bone mechanical loading during routine physical activities, reduced breast cancer cell extravasation across endothelial monolayers. Endothelial cells are situated at an ideal location to mediate signals between osteocytes in the bone matrix and metastasizing cancer cells in the blood vessels. Therefore, we used RNA sequencing to show that CM from endothelial cells conditioned in CM from flow-stimulated osteocytes significantly altered gene expression in bone-metastatic breast cancer cells. This This provides insights into the capability of bone-loading activity in preventing bone metastases.

2018-11-13 | E-MTAB-7176 | biostudies-arrayexpress

Prognostic value of biomarkers EpCAM and B-crystallin is associated with lymphatic metastasis in breast cancer by iTRAQ analysis

Project description:Metastasis is responsible for the majority of deaths in a variety of cancer types, including breast cancer. Although several factors or biomarkers have been identified to predict the outcome of patients with breast cancer, few studies have been conducted to identify metastasis-associated biomarkers. Quantitative iTRAQ proteomics analysis was used to detect differentially expressed proteins between lymph node metastases and their paired primary tumor tissues from 23 patients with metastatic breast cancer. Immunohistochemistry was performed to validate the expression of two upregulated (EpCAM, FADD) and two downregulated (NDRG1, αB-crystallin) proteins in 190 paraffin-embedded tissue samples. These four proteins were further analyzed for their correlation with clinicopathological features in 190 breast cancer patients. We identified 637 differentially regulated proteins (397 upregulated and 240 downregulated) in lymph node metastases compared with their paired primary tumor tissues. Furthermore, bioinformatics analysis using GEO profiling confirmed the difference in the expression of EpCAM between metastases and primary tumors tissues. Two upregulated (EpCAM, FADD) and two downregulated (NDRG1, αB-crystallin) proteins were associated with the progression of breast cancer. Obviously, EpCAM plays a role in the metastasis of breast cancer cells to the lymph node. We further identified αB-crystallin as an independent biomarker to predict lymph node metastasis and the outcome of breast cancer patients.

2019-08-29 | PXD013931 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data